The present invention relates generally to the field of particle analyzers, and more specifically to reducing noise in data collected from multi-parameter particle analyzers.
Particle analyzers enable analysis of properties of particles, for example, individual cells, by subjecting them to an excitation light and measuring the resulting scattered and/or emitted light as detected by one or more light detectors. Different types of particle analyzers, such as flow cytometers and scanning cytometers, are described in the art. In a flow cytometer, for example, the excitation light beams may be stationary, while analyte cells in a liquid flow through a point at which the light beams converge. A scanning cytometer scans a fixed cell population, for example, on a microscope slide, with one or more excitation light beams. Flow cytometers can also be equipped with sorting devices that have the added advantage of being able to separate individual cells in a sample for further culture or analysis.
Prior to being exposed to an excitation light, particles may be labeled (also referred to as marked) with spectrally distinct fluorescent dyes or fluorescent dyes conjugated to molecule-specific ligands. In a sample, each particle may bind with one or more fluorescent dyes and/or dye conjugates. For example, a single cell may bind with one or more fluorescent dyes conjugated to antigen-specific antibodies depending on the characteristics of the proteins that are elements of that cell. When an excitation light is focused on a cell in a flow cytometer, the cell may scatter and/or emit light in several directions. The pattern of the scattered and/or emitted light allows one type of cell to be distinguished from another. The resulting fluorescence pattern is generally indicative of defined characteristics of the particles under analysis. Samples of particles are generally labeled with multiple dyes or dye conjugates in order to identify a range of properties of the constituent particles. The measure of fluorescence of a particle that is not stained with any fluorescent dye, is known as the auto-fluorescence of that particle. However, while auto-fluorescence may enable the study of some properties of particles, by itself it is generally of limited use due to multiple factors including a fluorescence-level that is too low and the limited types of elements in a cell that may contribute to auto-fluorescence.
A particle analyzer may include multiple light detectors, and dyes are selected so that the peak fluorescence range of each dye is detected by a separate light detector. Due to the very low intensity of the kind of light emitted by small particles, these instruments are generally equipped with very sensitive detectors called photomultiplier tubes (PMTs) that can detect individual photons. The resulting fluorescence measurement is based on the number of the photons detected by each light detector.
The spectral range of each light detector in a particle analyzer is referred to as a “bin”. The clarity of the detected fluorescence data, as measured by light detectors, is dependent on each bin substantially containing only the measurements for the dye having a peak fluorescence within that bin. When a particle labeled with more than one dye is subjected to an excitation light, the light emitted by each dye may spread over a wide spectral range centered at or near the peak fluorescence maximum. The range over which the resulting fluorescence spreads may exceed the spectral range of the corresponding bin, and may also be detected in adjacent bins. This effect is referred to variously as “spillover,” “fluorescence spectral overlap,” or “dye spectral overlap”. The fraction of the signal that crosses over to another bin is known as the “spillover coefficient”.
As the number of dyes are increased and/or the fluorescence range of each dye is increased, the spectral overlap among the bins increase. The resulting data may have bins for which substantial spillover occurs. Substantial amounts of spillover may limit the usefulness of the data for particle analysis.
Methods are known in the art for utilizing multicolor particle analyzer data despite the spillover issues. The methods are based primarily on compensating for the spillover effect in the data to alleviate issues of overlapping data measurements in different detectors. Although in some situations compensation may be performed manually using analogical electronic circuitry, current compensation systems for higher number of colors and detectors may require software implemented compensation methods to be practical. Compensating for a spillover effect may involve subtracting some numerical quantity from each detector other than the primary detector for that color. For example, if a particle with a primary detector p1 has a spillover coefficient of 0.3 with respect to a second detector p2, then 30% of p1 must be subtracted from p2. This kind of linear compensation for spillover effect, in a multicolor particle analyzer, may involve solving complex sets of linear equations. A number of linear compensation methods are discussed in Bagwell, C. B., and Adams, E. G., “Fluorescence Spectral Overlap Compensation for any Number of Flow Cytometer Parameters,” Ann N.Y. Acad Sci. 677:167-84 (1993), and in Shapiro, H. M., “Practical Flow Cytometry,” 3rd edition, pp. 17-19, 163-166, 214-215 (Wiley-Liss, Inc., 1995).
Current linear approaches to digital compensation significantly improves the quality of the data analysis and often produces the desired two-dimensional orthogonal plot distribution that facilitates the identification of positive and negative particle populations. These known methods, prior to the invention, however, have limitations. Built into them, is an assumption that the spillover effects are linear, and therefore can be compensated for by such linear mathematical methods. Frequently when bright signals are detected using fluorescent dyes with relatively large spillover coefficients, a substantial amount of noise with non-linear characteristics remains after the compensation process.
Due to the large dynamic range of the measured fluorescence signal intensities, often in the range of four log decades or 10,000:1, linear display of a large amount of data would be of little clarity, particularly in the presence of spillover. In general, an exponential curve in the linear domain, appears as a linear curve in the logarithmic domain. The logarithmic scale is therefore able to display a large dynamic range such as 10,000:1 and is used very frequently to display and analyze particle data. However, particle analysis data containing noise that makes it deviate from linear behavior, once subjected to compensation, may contain a number of negative data points. Mathematically such negative data points are undefined on a logarithmic scale and are often lost or just shown as a collection of points on a display axis, limiting the usefulness of those data points to the analysis. On the other hand, abnormal data spreading due to non linear noise can produce visualization artifacts that produce false results at the moment to evaluate particle characteristics. More details on these issues can be found in Roederer, M., “Spectral Compensation for Flow Cytometry: Visualization Artifacts, Limitations, and Caveats,” Cytometry 45:194-205 (2001).
Known methods for utilizing particle data containing spillover issues as the previously discussed include a number of display oriented methods. For example, scaling systems, such as the biexponential model (Logicle) display described in U.S. Pat. No. 6,954,722, rely upon display techniques to present a useful display of the data addressing some issues of the pure logarithmic scale display discussed above. The biexponential model applies a linear scaling for lower intensity data points and a logarithmic scaling for higher intensity data points that alleviates the problem of inability to show negative data points in a pure logarithmic scaled display. However, the methods described in the art, while helping to clarify the raw data received from a particle analyzer, do not yield sufficiently low-noise-data for analysis. They do not address the root causes of the non linear data spreading and so can not solve the issue of establishing consistent positive/negative boundaries and determining accurate fluorescence values to prevent false positives.
A primary cause of the remaining non-linear noise is photon counting statistics error, also known as photon count statistic noise or shot noise. Photon counting statistics error refers to the error inherent in counting of photons captured by light detectors. See Roederer, supra, for additional information. Applying linear compensation by subtracting a percentage of the measured signal value at the primary detector from the measured value at a non-primary detector based on observed intensities, may result in reducing the measured value at the non-primary detector well beyond the linear spillover component. This aberration of the data caused by the remaining non-linear noise is particularly evident when the data is displayed in a logarithmic scale. Such overcompensation is likely because the subtracted value may have included a factor for the non-linear photon counting statistics error in addition to the linear spillover compensation factor. That remaining noise further complicates data analysis, and leads to identification of false positive populations. What is needed therefore, is a method and system, to substantially remove remaining noise components of particle data, particularly non-linear photon counting statistics error.